use crate::entities::order;
use crate::proto::OptimizationObjective;
use anyhow::Result;

pub struct RouteOptimizer;

pub struct OptimizationResult {
    pub order_sequence: Vec<String>,
    pub total_distance: f64,
    pub estimated_time: f64,
    pub estimated_cost: f64,
}

impl RouteOptimizer {
    pub fn new() -> Self {
        Self
    }

    pub async fn optimize_route(
        &self,
        orders: &[order::Model],
        objective: OptimizationObjective,
    ) -> Result<OptimizationResult> {
        // 简化的路线优化算法实现
        // 在实际应用中，这里会使用更复杂的算法如遗传算法、禁忌搜索等
        
        let mut order_sequence: Vec<String> = orders.iter().map(|o| o.id.clone()).collect();
        
        match objective {
            OptimizationObjective::MinimizeDistance => {
                // 基于距离的简单优化
                self.optimize_by_distance(&mut order_sequence, orders).await?;
            }
            OptimizationObjective::MinimizeTime => {
                // 基于时间的优化
                self.optimize_by_time(&mut order_sequence, orders).await?;
            }
            OptimizationObjective::MinimizeCost => {
                // 基于成本的优化
                self.optimize_by_cost(&mut order_sequence, orders).await?;
            }
            OptimizationObjective::MinimizeFuel => {
                // 基于燃油的优化
                self.optimize_by_fuel(&mut order_sequence, orders).await?;
            }
        }

        // 计算优化结果的指标
        let total_distance = self.calculate_total_distance(&order_sequence, orders).await?;
        let estimated_time = self.calculate_estimated_time(&order_sequence, orders).await?;
        let estimated_cost = self.calculate_estimated_cost(&order_sequence, orders).await?;

        Ok(OptimizationResult {
            order_sequence,
            total_distance,
            estimated_time,
            estimated_cost,
        })
    }

    async fn optimize_by_distance(
        &self,
        order_sequence: &mut Vec<String>,
        orders: &[order::Model],
    ) -> Result<()> {
        // 简单的最近邻算法
        if orders.len() <= 1 {
            return Ok(());
        }

        // 这里应该实现真正的距离计算和优化算法
        // 为了演示，我们只是按照订单ID排序
        order_sequence.sort();
        
        Ok(())
    }

    async fn optimize_by_time(
        &self,
        order_sequence: &mut Vec<String>,
        orders: &[order::Model],
    ) -> Result<()> {
        // 基于时间窗的优化
        // 按照配送时间窗排序
        let mut indexed_orders: Vec<(usize, &order::Model)> = orders.iter().enumerate().collect();
        
        indexed_orders.sort_by(|a, b| {
            let time_a = a.1.delivery_time_window_start.unwrap_or_default();
            let time_b = b.1.delivery_time_window_start.unwrap_or_default();
            time_a.cmp(&time_b)
        });

        *order_sequence = indexed_orders.iter().map(|(_, order)| order.id.clone()).collect();
        
        Ok(())
    }

    async fn optimize_by_cost(
        &self,
        order_sequence: &mut Vec<String>,
        orders: &[order::Model],
    ) -> Result<()> {
        // 基于成本的优化
        // 这里可以考虑重量、体积等因素
        let mut indexed_orders: Vec<(usize, &order::Model)> = orders.iter().enumerate().collect();
        
        // 按重量排序，重的先配送
        indexed_orders.sort_by(|a, b| {
            b.1.weight.partial_cmp(&a.1.weight).unwrap_or(std::cmp::Ordering::Equal)
        });

        *order_sequence = indexed_orders.iter().map(|(_, order)| order.id.clone()).collect();
        
        Ok(())
    }

    async fn optimize_by_fuel(
        &self,
        order_sequence: &mut Vec<String>,
        orders: &[order::Model],
    ) -> Result<()> {
        // 基于燃油消耗的优化
        // 类似于距离优化，但考虑车辆负载对燃油消耗的影响
        self.optimize_by_distance(order_sequence, orders).await
    }

    async fn calculate_total_distance(
        &self,
        _order_sequence: &[String],
        orders: &[order::Model],
    ) -> Result<f64> {
        // 简化的距离计算
        // 在实际应用中，这里会调用地图API计算真实距离
        Ok(orders.len() as f64 * 10.0) // 假设每个订单平均10公里
    }

    async fn calculate_estimated_time(
        &self,
        _order_sequence: &[String],
        orders: &[order::Model],
    ) -> Result<f64> {
        // 简化的时间计算
        // 包括行驶时间和装卸时间
        let driving_time = orders.len() as f64 * 0.5; // 假设每个订单平均0.5小时行驶时间
        let loading_time = orders.len() as f64 * 0.2; // 假设每个订单平均0.2小时装卸时间
        Ok(driving_time + loading_time)
    }

    async fn calculate_estimated_cost(
        &self,
        _order_sequence: &[String],
        orders: &[order::Model],
    ) -> Result<f64> {
        // 简化的成本计算
        // 包括燃油成本、人工成本等
        let fuel_cost = orders.len() as f64 * 50.0; // 假设每个订单平均50元燃油成本
        let labor_cost = orders.len() as f64 * 30.0; // 假设每个订单平均30元人工成本
        Ok(fuel_cost + labor_cost)
    }
}